City Warmth Island Research – Do Pristine Climate Stations Even Exist? Half Two. ‘HIDE THE INCLINE’. – Watts Up With That?

By: Geoff Sherrington.

Scientist, Australia.

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Part 2 of this article starts with the simple concept equation for UHI raised in Part 1.

Tuhi = Turban – Tpristine

Part 1 showed some scatter in the temperature trends of 45 “pristine” candidate stations. For the equation to work, urban stations need be examined in similar ways, so that their subtraction means something. This can be done on broad scale – here the whole of Australia – or detailed scale, such as a city centre measured against its suburbs.

We start with broad scale.

From a tabulation of the start dates of more than 1,500 Australian weather stations, I did a subjective selection of “urban” candidates based on the length of the data and the amount of missing data (while avoiding already selected “pristine” stations). There are many short-term stations and many with large amounts of data missing, so the number of stations came down to a mere 37. This selection was done correctly before calculation of temperature trends and was not affected by them.

Some relevant properties of these 37 stations, plus the 45 pristine stations from part 1, are tabulated here. (The table does not fit well into this .docx Word article). The properties include latitude, longitude, altitude, distance from the ocean, population, rainfall, length of data years, % of days with missing values, RMS error estimates, to name some of them. Readers can enter the lat/long numbers directly into Google Earth to view the surroundings.

https://www.geoffstuff.com/final82properties.xlsx

The derived time trends for urban temperatures, years 1910 to 2020, are summarised below:

URBAN	URBAN Trend Tmax deg C/Century, daily data. 1910 to 2020	URBAN Trend Tmin deg C/Century, daily data. 1910 to 2020	URBAN Tav trend ⁰C per century, derived.	URBAN Tmax RMS ERROR all years	URBAN Tmin RMS ERROR all years
Adelaide	-0.1	0.0	0.0	6.49	4.65
Alice_Springs	1.5	0.4	1.0	7.38	7.52
Bathurst	1.4	-0.6	0.4	7.05	5.85
Boulia	0.7	1.8	1.3	6.89	6.91
Bourke	0.3	0.6	0.5	7.66	6.86
Broome	0.7	0.3	0.5	3.19	5.38
Bundaberg	0.7	1.2	1.0	3.49	4.81
Burketown	1.4	1.3	1.4	3.67	4.98
Cairns	0.3	1.2	0.8	2.74	3.24
Charters_Towers	0.4	1.3	0.8	4.44	4.62
Cobar	0.4	1.4	0.9	7.91	6.62
Darwin	0.1	-0.3	-0.1	1.84	2.77
Deniliquin	0.8	-0.6	0.1	7.55	5.74
Esperance	1.2	-1.1	0.1	5.18	3.95
Gayndah	0.6	2.2	1.4	4.82	6.07
Georgetown	1.1	1.1	1.1	3.67	5.15
Geraldton	2.0	-1.1	0.4	5.74	4.67
Inverell	0.6	0.9	0.7	6.18	6.73
Kalgoorlie	-0.1	0.1	0.0	7.39	5.73
Kerang	0.8	0.9	0.8	7.53	5.33
Longreach	0.8	1.2	1.0	6.21	6.68
Marble_Bar	-0.1	0.8	0.4	6.19	5.96
Melbourne	1.2	2.2	1.7	6.15	4.13
Mildura	0.5	0.0	0.2	7.44	5.65
Miles	0.7	1.1	0.9	6.06	6.96
Normanton	0.2	1.2	0.7	3.51	4.39
Perth	2.7	-0.7	1.0	6.07	4.51
Port_Macquarie	2.0	0.0	1.0	3.68	4.98
Richmond_Qld	0.6	1.7	1.1	5.29	6.19
Sale	0.4	0.1	0.2	5.69	4.63
Snowtown	0.9	-0.5	0.2	7.34	5.08
Sydney	1.5	1.6	1.5	4.53	4.41
Tennant_Creek	0.2	1.9	1.1	5.87	5.61
Tibooburra	0.9	1.8	1.3	7.84	6.97
Wagga_Wagga	-0.3	-0.2	-0.2	7.76	6.21
Walgett	1.1	0.0	0.5	7.29	7.02
Yamba	1.3	0.7	1.0	3.43	4.44
Simple average	0.8	0.7	0.7	5.71	5.44

A graphical representation of the urban stations Tmax with linear trend lines follows:

As for pristine stations in Part 1, here are the lightly smoothed trends derived from the daily maximum temperatures Tmax of the 37 urban stations. Readers are invited to imagine their own form of UHI effect, to see how easy it might be to find among these lines.

The wriggles do not match well from station to station, thought there might be a negative dip in years 2010 or 2011, which we examine later in this article.

Compared to the pristine data set, the urban data set from Part One shows lower trends with time, as in the simple average.

		SIMPLE AVERAGES OF TRENDS
	Tmax trend	Tmin trend	Tav trend	Tmax RMS	Tmin RMS
PRISTINE	1.5	1.3	1.4	4.54	4.14
URBAN	0.8	0.7	0.7	5.71	5.44

Overall, a naïve interpretation is that warming is slower in urban places than in pristine places, so that UHI might affect remote places more than cities. This might be because UHI had already affected the urban stations before 1910, but was still happening after 1910 at some of the pristine stations. It would depend on the magnitude of the UHI being large enough to be seen through the noise in the data. Noise is expressed by the various wriggles that are out of phase with each other from station to station. There is interest in the cause of the noise.

So far, the article has dealt with national scale illustrations. Moving now to local scale, as an example for only one city, we look at Melbourne station, 86071 and seek an explanation for the dip in 2010-11. This was a Regional Office until 2014, when Olympic Park 86336 some 2.6 km distant, replaced it in early June, 2013.

By a Press Release on 9th December 2014, the BOM announced:

Tarini Casinader, Victoria Regional Director, said the Bureau had opened the upgraded weather station at Olympic Park in November 2013, and had kept the two sites running in tandem for over twelve months to ensure a smooth transition.

“The La Trobe Street weather observation station, located at the Royal Society of Victoria, has been operating since 1908. However, wind recordings were stopped in 2009 due to the poor quality of readings as a result of nearby building development at the city location,” said Ms Casinader.

“The location at Melbourne Olympic Park represents an improvement in the quality of meteorological observations available, and meets international observing standards set out by the World Meteorological Organisation.”

https://media.bom.gov.au/releases/56/olympic-park-replaces-la-trobe-street-as-home-of-melbournes-weather/

The following graph shows the overlap period when both stations were recording temperatures in parallel. This is for Tav, which is (Tmax + Tmin)/2. Note the “gap” of about 1.5 ⁰C in the overlap time.

It had been obvious that since 1910, Melbourne Regional was observing too hot. Here is a graph with 22 stations within 80 km or so of Melbourne Regional. Daily surrounding station observations are here subtracted from Melbourne Regional daily observations to show the difference. It is probable that Melbourne is the odd-man-out, the one station rather different to most of the other 22.

When Australia’s Bureau of Meteorology started adjustments to its records with the ACORN-SAT methods in late 2018, Melbourne Regional was adjusted. This next graph shows the raw Melbourne daily data minus the ACORN-SAT adjusted data, to highlight the difference. Years 1970 to 2020 are shown for brevity.

It is difficult to derive reasons for step change adjustments of this magnitude, to near 1⁰C from equality for Tmax here. Automatic weather station apparatus (MMTS for some) using electronic thermocouples instead of Liquid-In-Glass thermometers, could explain some of the 1991-1993 event, but not why they change at different dates for Tmax and Tmin; the 2013 event coincides with the station move from central Melbourne to the more suburban Olympic Park.

On the other hand, it is easy to concoct a narrative that the Melbourne Regional observations were becoming artificially too hot over the years, that something had to be done to cause a decline. Why not change the instruments and the location of the station? Why not join the new Olympic Park data to the end of the old Regional Office data and label it as Melbourne Olympic Park, for that is the title of its data that can be downloaded from the BOM ACORN-SAT web site. It covers years 1910 to the present under that name, despite Olympic Park starting in 2013.

The theme of these articles is whether the historic temperature record is useful for understanding Urban Heat Island Effects. For Australia, Melbourne is important because it has been situated close to a major BOM office, with research facilities, a huge computer and access to CSIRO and universities for some years. It should produce an example an example of high quality, minimal error, dependable observations, among the best in Australia.

It will be interesting in coming years to observe how that 1 ⁰C or so difference is smoothed to a respectable appearance. It is also interesting to speculate how one can find evidence of a UHI effect in this noisy and plausibly misleading data.

In humour, could this be named “Hide the Incline?”                          (END)

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